Skip to main content
Join the official 2019 Python Developers SurveyStart the survey!

Alternating Conditional Exceptation with Neural Network

Project description

ace_nn

Build Status

Introduction

This repo contains experimental implementation of ace algorithm via neural network. It is shown by xiangxiang-xu that calculating optimal features by Alternating Conditional Expectation is equivalent to maximize H-score.

How to run

Three examples are provided ( one for continuous variable and the other twos are for discrete variable) and their results are the same as ace. The main function is ace_nn and its parameters are very similar to ace_cream.

import numpy as np
from ace_nn import ace_nn
# discrete case, binary symmetric channel with crossover probability 0.1
N_SIZE = 1000
x = np.random.choice([0,1], size=N_SIZE)
n = np.random.choice([0,1], size=N_SIZE, p=[0.9, 0.1])
y = np.mod(x + n, 2)
# set both x(cat=0) and y(cat=-1) as categorical type
tx, ty = ace_nn(x, y, cat=[-1,0], epochs=100)

# continuous case
x = np.random.uniform(0, np.pi, 200)
y = np.exp(np.sin(x)+np.random.normal(size=200)/2)
tx, ty = ace_nn(x, y)

For more detail, run help(ace_nn) to see the parameters and returns of this function.

Further discussion

Currently, the neural networks used to approximate optimal $f(x)$ and $g(y)$ are two-layer MLP with tanh as activation function. More turns of epochs are needed for large alphabet $|\mathcal{X}|$ and $|\mathcal{Y}|$ and the running time is not short.

Also, batch_size and hidden_units_num can be hypertuned, and there is no guarantee that current configuration of neural network is optimal for solving ace.

Application

we can use ace_nn(x, y, return_hscore = True) to calculate a lower bound of $\frac{\norm{B}_F^2}{2}$

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for ace-nn, version 0.2
Filename, size File type Python version Upload date Hashes
Filename, size ace_nn-0.2.tar.gz (3.8 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page